Should heart-to-body energy consumption ratio equal oxygen consumption ratio?

The human heart pumps oxygen to the body, and the heart itself requires oxygen.

Both the body and the heart use energy, usually expressed in calories or ergs.

If we look at the energy consumption (in Watts) of the heart as a fraction $F$ of the energy consumption of the body as a whole it is about

$F approx frac{1.3}{91.3}=0.014.$

Humans consume about 550L of oxygen per day per this website and the heart itself consumes about 8 ml of $O_2$ per 100g per minute at rest according to this site. Assuming an average global human heart mass of about 310g (Google) the daily total heart oxygen consumption is about (8ml x 3.1 x 1440 min) = 35712 ml or 35.7L of oxygen. The ratio $R$ of heart to body oxygen consumption is thus about

$R approx frac{36}{550-36} = frac{36}{514} =0.07$

These figures are rough at best but not rough enough to account for a discrepancy of a factor of 5 in the ratios. Can someone suggest what might account for the difference?

The calculation seems to show that the heart's proportional consumption of oxygen is a lot higher than its proportional consumption of energy. My understanding is that the respective oxygen consumption of the heart and body are a good reflections of their respective energy consumption. Is that a sound assumption?

Thanks for any insights.

At first glance there are a few major issues with your calculation and assumptions.

  1. You're comparing the energy output in work (pumping action) with the energy consumption of the rest of the body in input (oxygen consumption). I don't know how efficient the heart is, but I'd guess only somewhere from 20-40% of input energy is actually converted to pumping action.

  2. Oxygen consumption is correlated with energy consumption, and would be a good way to measure energy consumption if you know exactly what the subject is burning. However, if you have a big difference between the preferred fuels between the heart and the rest of the body, this doesn't really apply anymore. A quick google leads me here:, showing that the heart prefers to burn fat over glucose, and this creates a significant difference:

    Carbohydrate oxidation typically generates approximately 120 kcal per mole of respired oxygen, whereas fatty acid oxidation typically generates only approximately 100 kcal per mole of oxygen.

This means that the heart needs more oxygen than the rest of the body to generate the same amount of energy.

I think combined this could explain the 5-fold difference.

You are comparing the theoretical mechanical work performed by the heart (your 1.33 W number) with the chemical energy consumed in accomplishing that in a biological system. One is theoretical and one is empirical. The heart in fact consumes about 10% of the energy of the body even though it's only 1% of the weight, owing to the special nature of cardiac muscle tissue (which is unique to the heart organ). It's a “design” that can go nonstop for one billion contractions or more, and which would have been ludicrously inefficient to use for skeletal musculature. Your 5x factor is in line with typical efficiency measurements of muscle work, around 20-25%.

Myocardial Oxygen Consumption

Alexios S. Antonopoulos , . Dimitris Tousoulis , in Coronary Artery Disease , 2018


Experimental studies have demonstrated that the heart rate, wall tension and contractility (or the velocity of contraction) are all among the major determinants of myocardial oxygen consumption (MVO2). The basal O2 consumption of the arrested heart is approximately 20% that of the contracting heart. On the other hand the O2 requirements for myocyte depolarization are only 0.5% of the working heart. Studies investigating the relative oxygen costs of cardiac output and aortic pressure have further enhanced our understanding of the physiology of MVO2. The oxygen costs for “pressure work” seem to be much higher than of “flow work”. Also, the velocity of contraction seems to be another critical determinant of the myocardial oxygen needs. In similar interventional experimental studies involving cardiac catheterization in animals or humans various methods have been proposed to indirectly calculate MVO2 or the supply:demand ratio. Even though these markers have not been incorporated in clinical practice, they have contributed to the better understanding of MVO2 in human health and disease, and how to apply the appropriate therapeutic interventions to lower myocardial oxygen needs.

Respiratory Quotient (RQ): Study Notes

Respiratory quotient is the ratio of the volume of carbon dioxide produced to the volume of oxygen consumed in respiration over a period of time. Its value can be one, zero, more than 1 or less than one.

RQ = Volume of C02 evolved/Volume of 02 absorbed

RQ Equal to Unity:

Respiratory quotient is equal to unity if carbohydrates are the respiratory substrate and the respiration is aerobic.

RQ Less than Unity:

RQ is less than one when respiration is aerobic but the respiratory substrate is either fat or protein. RQ is about 0.7 for most of the common fats. It occurs during germination of fatty seeds.

RQ is about 0.9 in case of proteins, peptones, etc.

Succulents do not evolve carbon dioxide during night (when their stomata are open) as the same is used in carbon fixation. They also change carbohydrates to organic acids which utilise oxygen but do not evolve carbon dioxide.

RQ More than Unity:

(a) RQ slightly more than unity is found when organic acids are broken down as respiratory substrates under aerobic conditions, e.g.,

(b) In anaerobic respiration there is no consumption of oxygen. Carbon dioxide is produced in most of the cases. Therefore, respiratory quotient is infinity. Carbohydrate is the usual substrate.

An intermediate value is obtained where an organ is undergoing both aerobic and anaerobic modes of respiration.

(i) Knowledge of respiratory quotient helps in determining respiratory substrate.

(ii) It helps in knowing the type of respiration being performed,

(iii) It provides some information about major transformation of food materials.


Climate change has increased the average temperature as well as the frequency and amplitude of thermal fluctuations in ecosystems on a global scale (IPCC, 2013 ). Altered temperature dynamics are changing species distribution and phenology (Durant et al., 2007 Perry, 2005 Pinsky et al., 2013 Winder & Schindler, 2004 ) and can ultimately lead to ecological regime shifts (Walther et al., 2002 Wernberg et al., 2016 ). A decline in body size has been proposed as yet another universal response to climate warming with great impact on population dynamics and species interactions, and, in turn, ecosystem dynamics (Gardner et al., 2011 Ohlberger, 2013 Sheridan & Bickford, 2011). This also applies to fish, where decreasing body size in relation to environmental warming has been reported for a vast range of species (Genner et al., 2010 Jeppesen et al., 2010 Baudron et al., 2014 Rijn et al., 2017 Audzijonyte et al., 2020).

As optimum temperature for growth and growth efficiency have been shown to decline with increasing fish size in several species (Björnsson et al., 2001 Björnsson & Tryggvadóttir, 1996 Imsland et al., 2006 ), physiological or morphological mechanisms have been proposed to be underlying the decline in fish size with increasing environmental temperature (Angilletta Jr & Dunham, 2003 ). Aerobic metabolism provides the main part of the energy used for maintaining homeostasis, locomotion and digestion, which eventually influences growth (Brown et al., 2004 Claireaux & Lefrançois, 2007 McKenzie & Claireaux, 2010 ). Aerobic metabolism is bordered by two extremes: the minimum, or resting, level of metabolism a fish needs to maintain homeostasis, termed the standard metabolic rate (SMR Chabot et al., 2016 ), and the highest achievable rate of metabolism, usually obtained by exhaustive exercise, called the maximum metabolic rate (MMR Norin & Clark, 2016 ). The difference between SMR and MMR (MMR − SMR) defines the potential metabolism that can support physiological activities, such as digestion, growth and locomotion, and is aptly called the aerobic scope (Fry, 1971 ). SMR, MMR and aerobic scope increase allometrically with fish body mass by exponents normally between 0.8 and 0.9 (Clarke & Johnston, 1999 Killen et al., 2016 ). However, the magnitude by which MMR can increase above SMR (called the factorial aerobic scope) stays virtually the same with increasing body mass, at least on an interspecific level (Lefevre et al., 2017 ). The mass scaling exponents of SMR have been shown to decrease with increasing temperature in several fish species (Li et al., 2018 Ohlberger et al., 2012 ). However, to what extent temperature affects mass scaling of MMR, aerobic scope and factorial aerobic scope remains largely unexplored to date, only Tirsgaard et al. ( 2015 ) has examined the combined effect of temperature and fish size on SMR and MMR within a species, showing that SMR scaled with a higher exponent than MMR in Atlantic cod Gadus morhua L. 1758 at higher temperatures. Consequently, the temperature of highest aerobic scope decreased with increasing fish size. Both size and temperature have vast influence on fish physiology and ecology, and are central components of ecological modelling, especially in relation to climate change (Cheung et al., 2013 ). Interactions between temperature and size scaling of metabolic rates are therefore important to address.

Decline in body size with temperature increase is not uniquely related to climate change: animals generally decrease in size with increasing habitat temperature, for instance along latitudinal and altitudinal gradients (Atkinson & Sibly, 1997 Bergmann, 1948 ). This tendency is known as the temperature-size rule, or Bergmann's rule, and applies both among species and among populations of the same species. However, the temperature-size rule in fish is conceptually different from that in other animals as the thermal size segregation in fish also depends on ontogeny: small individuals (post-larvae) tend to inhabit warm and shallow waters, whereas large and old individuals stay in deep and cooler water (Morita et al., 2010 Rijn et al., 2017 ). In accordance with this, preferred temperature determined in laboratory settings has been shown to decrease with increasing fish size in G. morhua (Lafrance et al., 2005 ). Ontogenetic shifts in the thermal habitat of fish could be a way for larger individuals to behaviourally optimize ambient physiological conditions [cf the size-dependency of optimal temperature for growth and aerobic scope (Björnsson et al., 2001 Tirsgaard et al., 2015 )] and to avoid adverse effects of high temperature (Sunday et al., 2014 ). However, the size-dependency of preferred temperature has remained largely unexplored.

In a recent study, Huss et al. ( 2019 ) showed that smaller individuals of European perch Perca fluviatilis L. 1758 grew faster at higher temperatures while larger fish did not. The species is thus an ideal candidate for studying physiological and behavioural responses to increasing temperatures. P. fluviatilis is a eurythermal fish dispersed in fresh water and estuaries over most of the Eurasian continent (Couture & Pyle, 2015 Craig, 2000 Thorpe, 1977 ) and an ecologically important species by virtue of its top-down regulating effect in the ecosystem (Jeppesen et al., 2000 Ljunggren et al., 2010 ). The species is furthermore a target for commercial and recreational fishery (Couture & Pyle, 2015 Craig, 2000 Thorpe, 1977 ), and a candidate for aquaculture rearing (Overton et al., 2008 ).

The present study investigated the combined effects of temperature and body size on oxygen consumption rate [ṀO2 a proxy for aerobic metabolism (Nelson, 2016 )], and the size dependency of preferred temperature in P. fluviatilis. It was hypothesized that aerobic scope would be constrained in larger fish at higher temperatures and that the optimal temperature for aerobic scope and preferred temperature would decrease with increasing fish size.


Because gravitational forces are important, dynamic similarity in walking and running is possible only between animals travelling with equal Froude numbers. Alexander (1977)plotted relative stride length (stride length/leg length) against the square root of Froude number, for ostriches and various mammals, and found that all the points lay near a single line. Alexander and Jayes(1983) showed in more detail that mammals of different sizes, running with equal Froude numbers, tend to dynamic similarity: they use the same gait, similar relative stride lengths and duty factors, and exert similar patterns of force on the ground. There are some discrepancies (notably, rodents and other small mammals, which run with their legs more bent than larger mammals, and take relatively longer strides at the same Froude number), but the predictions of dynamic similarity hold reasonably well. Biewener(1989) pointed out that larger mammals need to run on straighter legs than small ones, to avoid excessive bone and muscle stresses. Birds of different sizes, running at equal Froude numbers, also tend to move in dynamically similar fashion, with discrepancies due to the largest birds keeping their legs straighter(Gatesy and Biewener,1991).

Some of the energy that mammals and birds would otherwise need for running is saved by tendons that store and then return elastic strain energy, in the course of a step. Alexander(1988) pointed out that, for dynamic similarity in running, animals should be elastically similar in other words, forces proportional to their body weights should cause equal strains(fractional length changes). Bullimore and Burn(2004) showed that this presents a problem, because tendon has the same elastic modulus in mammals of all sizes. Elastically similar structures undergo equal strains (change of height/height) when loaded with their own weight. Structures with equal elastic moduli loaded with their own weight, however, undergo strains in proportion to the stress, which is (weight/cross sectional area). If they are geometrically similar, their weights are proportional to (length) 3 and their cross-sectional areas to (length) 2 . Therefore elastic similarity, between mammals of different sizes whose tendons have equal elastic moduli, is inconsistent with strict dynamic similarity. Bullimore and Burn (2004) went on to show that the size-related changes in posture that Biewener(1989) had shown to be necessary to avoid excessive stresses in large mammals, also made approximate elastic similarity possible.

For animals running in dynamically similar fashion, all forces are proportional to body weight and all velocities to the speed of running. Thus mechanical power is proportional to (weight × speed), and the mechanical cost of transport [power/(mass × speed)] is independent of body mass. Alexander (1977) showed that cost of transport was independent of mass for specific models of walking and running.

Taylor et al. (1982) showed for a wide range of mammals and birds that the metabolic power required for running was linearly related to speed. They subtracted the intercept at zero speed (representing the metabolic rate while standing still) to obtain the net power required for running. They found that the net metabolic cost of transport was proportional to body mass(Mb) -0.32 . Fig. 1 shows that the same relationship also fits data from reptiles, amphibians and arthropods(Full, 1989). Calculations based on force plate records and films of a smaller sample of birds, mammals and arthropods showed, however, that the mechanical cost of transport was independent of body mass (Heglund et al.,1982 Full and Tu,1991), as predicted by the dynamic similarity model.

Graphs of cost of transport against body mass Mb for running animals. The upper line shows the net metabolic cost, and the lower one the mechanical cost (ignoring fluctuations of internal kinetic energy, and energy saving by elastic mechanisms). On the horizontal axis, 0.1 mg should read 0.1 g. From R. J. Full: Mechanics and energetics of terrestrial locomotion: bipeds to polypeds. In Energy Transformations in Cells and Organisms (ed. W. Wieser and E. Gnaiger), pp. 175-182. Stuttgart: Thieme. Reprinted by permission.

Graphs of cost of transport against body mass Mb for running animals. The upper line shows the net metabolic cost, and the lower one the mechanical cost (ignoring fluctuations of internal kinetic energy, and energy saving by elastic mechanisms). On the horizontal axis, 0.1 mg should read 0.1 g. From R. J. Full: Mechanics and energetics of terrestrial locomotion: bipeds to polypeds. In Energy Transformations in Cells and Organisms (ed. W. Wieser and E. Gnaiger), pp. 175-182. Stuttgart: Thieme. Reprinted by permission.

The efficiency with which the muscles perform the work required for running is the work divided by the metabolic energy cost. Fig. 1 seems to show that large runners are more efficient than small ones that the efficiency of running is approximately proportional to Mb 0.3 . The mechanical costs shown in Fig. 1, however, ignore the savings made by elastic mechanisms, leading to the impossible prediction (by extrapolation) of efficiencies greater than 100% for animals of the size of elephants. The apparent increase of efficiency with increasing body size could be misleading if savings by elastic mechanisms are proportionately larger in larger animals. Apparent efficiencies calculated from the data of Fig. 1 are 17 times greater for 100 kg runners than for 10 g runners. It seems most unlikely that any difference in the effectiveness of elastic mechanisms, between runners of different sizes, is large enough to account for that. We must conclude that the muscles of larger runners do indeed perform work with higher efficiency. Before discussing this further, we will ask whether the same is true for other modes of locomotion.


While there are numerous methods for measuring the energy expenditure of marine mammals in the field, not all are equal in reliability, precision or ease of use. The three most common current methods employ measures of doubly labelled water (DLW) turnover, body acceleration metrics and heart rate (fH). The DLW method provides a mean estimate of field metabolic rate over a finite period that is not activity specific, with individual error estimates in pinnipeds ranging from –39 to +44% (Sparling et al., 2008). Body acceleration metrics, such as overall dynamic body acceleration (ODBA) and flipper stroking, are limited to predicting metabolic rate during active behaviours, and cannot account for changes in physiological state such as digestion (Bevan et al., 1997 Hays et al., 2004 Williams et al., 2004 Fahlman et al., 2008b Green et al., 2009 Halsey et al., 2009). The fH method estimates oxygen consumption rate (, an indirect measure of energy expenditure) from measured fH (Fick, 1870). fH can therefore provide activity-specific estimates of energy expenditure on a much finer time scale and for longer periods of time than the DLW method (Boyd et al., 2004 Butler et al., 2004 Ponganis, 2007), with comparable error estimates to ODBA under steady-state conditions (Green et al., 2009 Halsey et al., 2009).

The fH method requires deriving species-specific predictive equations between fH and in a controlled environment before the technique can be used to predict in the field (for a review, see Green, 2010). Previous studies in marine mammals and birds have made comparisons while animals were submerged in a shallow swim mill (Woakes and Butler, 1983 Williams et al., 1991 Butler et al., 1992 Boyd et al., 1995 Ponganis et al., 1997 McPhee et al., 2003), walking on a treadmill (Froget et al., 2001 Green, 2001 Froget et al., 2002 Green et al., 2005), swimming horizontally in open water (Williams et al., 1993) or diving in a shallow tank (Bevan et al., 1992 Webb et al., 1998a). However, the artificial modes of locomotion and environments employed in these studies raise questions of applicability to animals that spend a considerable time diving to depth.

Despite the current use of fH to predict of diving marine mammals in the wild (i.e. Hindell and Lea, 1998 Boyd et al., 1999), it is not clear whether the fH method is accurate for pinnipeds foraging at natural depths and for realistic dive durations. Accurate estimates of metabolic rate in the wild require relationships that are derived under controlled conditions which encompass dive durations and dive depths that are representative of free-ranging animals. Previous studies that have investigated fH and in diving marine mammals have been limited by maximum tank depth (Webb et al., 1998a Sparling and Fedak, 2004). Our study derived relationships between fH and in trained Steller sea lions freely diving in the open ocean to depths up to 40 m and for durations of 1–6 min, which reflect dive characteristics comparable to those of free-ranging animals (Merrick and Loughlin, 1997 Rehberg et al., 2009). Our specific objectives were therefore to (1) simultaneously measure and determine the relationship between fH and while Steller sea lions were foraging and diving to depths of up to 40 m and (2) determine whether fH could be used to predict average metabolic rate (AMR) or diving metabolic rate (DMR) over either a single dive or a series of continuous dives (dive bout).


This study is the first to extensively examine the role of temperature on swimming energetics within and among different stocks of adult Pacific salmon under field and laboratory settings. With a total of 107 adult salmon tested,it is also the most comprehensive study of adult salmon swimming performance to date.

Although the blocking effect for a few of the fish was high, the Ucrit and O2max data obtained here for sockeye salmon are entirely consistent with earlier laboratory and field studies involving adult Pacific salmon (e.g.Brett and Glass, 1973Jones et al., 1974Williams et al., 1986). For example, Ucrit (2.41 BL s -1 , N=8) reported for smaller (1.65±0.07 kg) adult sockeye salmon(Brett and Glass, 1973) lies in the upper end of our Ucrit range, while our O2max data tend to be higher than theirs at corresponding temperatures(Fig. 3C). O2max (13.83 mg O2 kg -1 min -1 ) and Ucrit(2.33 BL s -1 ) for pink salmon(Williams et al., 1986) are comparable to the present study. The exponential relationships between O2max and temperature reported earlier for adult sockeye salmon either lie below(Davis, 1966) or above(Brett and Glass, 1973) a significant exponential relationship (r 2 =0.63Fig. 3C) that could be fitted to our O2maxdata. (Note: this exponential regression tended to over-represent WVR sockeye salmon and under-represent both ES and GC sockeye salmon, i.e. there was a poor fit for any of the individual salmon stocks.) The high quality of the present data was also illustrated by the repeatability of the swim tests,because RR decreases significantly (Jain et al., 1998 Tierney,2000) when rainbow trout Oncorhynchus mykiss and sockeye salmon are either sick or have been challenged by toxicants.

Temperature effects

The final temperature preferendum paradigm, proposed by Fry(1947), embodied three principal inferences: a species-specificity to the final temperature preferendum a relationship between the final temperature preferendum and field distribution and a relationship between final temperature preferendum and the temperature at which centrally important processes take place at maximum efficiency. Indeed, the concept of temperature optima for physiological processes related to swimming is well documented (see reviews byBeamish, 1978Houston, 1982Guderley and Blier, 1988Hammer, 1995Kelsch, 1996Johnston and Ball, 1997Kieffer, 2000). Furthermore,there is emerging evidence that maximum cardiac performance and the oxygen supplying the cardiac tissue may be `centrally important processes' (Farrell,1997,2002), though other processes are likely to be important (Pörtner,2002). The present results therefore extend the idea of temperature optima to include the possibility of stock-specific temperature optima, in addition to confirming an important temperature effect on the physiological processes that determine O2max, scope for activity and Ucrit. Three stocks of adult salmon demonstrated distinct temperature optima for O2max and scope for activity, while GC and WVR sockeye salmon also exhibited temperature optima for Ucrit. In contrast, Ucritfor CHE coho salmon displayed low temperature sensitivity. Temperature optima around 15°C have been reported previously for O2, metabolic scope and sustained cruising speed with juvenile and adult sockeye salmon(Brett and Glass, 1973). While this temperature is very close to the temperature optima reported here for GC sockeye salmon, there were clear differences in the temperature optima for WVR sockeye salmon (Fig. 3). Furthermore, the temperature optima adult CHE coho salmon are considerably lower than that reported earlier for juvenile coho salmon (approx. 20°CBrett et al., 1958), a difference that could reflect either a stock-specific effect or developmental effect.

Although temperature optima were clearly established for the salmon stocks,a measure of temperature insensitivity for peak swimming capability is likely to be critical for these salmon stocks because they routinely face varying water temperatures. To gauge temperature insensitivity we used the regression equations to arbitrarily estimate the temperature range over which a salmon stock could reach at least 90% of its peak O2max. These temperature ranges were: 14.7-20.3°C for GC sockeye salmon,12.7-17.3°C for WVR sockeye salmon and 5.0-11.4°C for CHE coho salmon. Using a similar analysis for scope for activity, the temperature ranges for the three stocks were similar to those for O2max, but marginally cooler and/or narrower (13.9-19.3°C for GC sockeye salmon,12.8-16.2°C for WVR sockeye salmon and 6.6-8.9°C for CHE coho salmon). This analysis clearly shows that these three salmon stocks can approach their respective peak aerobic activity over a temperature range spanning as much as 5°C. Such physiological flexibility may be adaptive (see Guderley and Blier, 1998) because the water temperature in the Fraser River may vary annually on a given date by as much as 6°C, perhaps even providing sufficient flexibility to handle all but the most extreme temperature conditions encountered in the Fraser River during a particular migration window. This does not mean that extreme temperature and/or hydrological conditions (known to occur in certain years) would not impose difficulties for migration (e.g. ES sockeye have faced water temperatures reaching 22°C and flows of 9000 m 3 s –1 Macdonald et al., 2000). But it does mean that physiological information provided here could be useful in predicting which river conditions are more likely to impair passage and reduce spawning success.

An equally important discovery was that these temperature optima correlated very closely with the ambient water temperature of the natal river for individual salmon stocks. This meant that the lowest thermal sensitivity of peak aerobic performance occurred around the ambient temperature of the natal stream. While environmental variability can differentially influence the ability of organisms to survive and reproduce, tailoring populations to their respective environmental niches (Cooke et al., 2001), and the `stock concept' suggests adaptation to local conditions (Berst and Simon,1981), migratory salmon spend most of their life away from the natal streams. Whether the present correlation between temperature optima and natal stream temperature is a reflection of adult salmon being pre-adapted to water temperatures likely to be encountered during river migrations, or is coincidental with the temperature preferendum of the species (e.g. for sockeye salmon 14.5°C, Brett, 195210.6-12.8°C, Horak and Tanner,1964), will require further study. Further work will also need to tackle the possibility of rather rapid thermal compensation during the actual in-river migration. For some sockeye salmon stocks, the timing of migration seems to have been far too restrictive for thermal compensatory processes to take full effect. For example, ES sockeye move from seawater in the Georgia Strait, where they encounter temperatures likely to be no warmer than 13°C, into river water as high as 18°C, and then within 4 days face one of their most difficult in-river swimming challenges, Hell's Gate.

Although the majority of tests were performed at ambient water temperature,small temperature adjustments were used to extend the ambient temperature range. Acclimations to these temperatures were necessarily short (5 days)because fully ripe salmon have compromised swimming ability(Williams et al., 1986). While the short acclimation period is a concern, the extent of the temperature change (<6°C) was not unusual compared with changes naturally encountered, because ES and Chilko stocks of adult sockeye salmon routinely face temperature changes of 1°C daily and as much as 7°C over 1 day during their migrations (Idler and Clemens, 1959). In addition, individual fish tested at either their ambient temperature or an adjusted temperature showed a reasonable overlap of O2maxvalues (Fig. 3A). A second concern is that we did not consider sex differences in swimming energetics. This concern is offset by the fact that we used equal numbers of male and female fish in many test groups. Furthermore, physiological telemetry studies of migrating ES sockeye salmon and Seton River pink salmon have revealed little difference between sexes in terms of the overall cost of transport to the spawning site, although males were less efficient at migrating through hydraulic obstacles (Hinch and Rand,1998 Standen et al., 2002). The present study could be used as a framework for future studies of sexual dichotomy in swimming capabilities.

The finding that O2routineincreased exponentially with temperature is consistent with previous studies showing exponential relationships for both O2routine and standard metabolic rate (rainbow troutDickson and Kramer, 1971),brown trout Salmo trutta Butler et al., 1992), sockeye salmon(Brett and Glass, 1973),tilapia Sarotherodon mossambicusCaulton, 1978) and largemouth bass Micropterus salmoides Cooke et al., 2001). As expected, our O2routine values for adult sockeye salmon were higher than the standard metabolic rate previously reported (Brett and Glass,1973). Some of this difference could be attributed to the on-going gonadal development in the mature fish used in the present study. It is also possible that the overnight recovery is insufficient (seeFarrell et al., 2003) and adult salmon are more restless than less mature fish. Williams et al.(1986) noted that adult pink salmon were more restless than sockeye salmon in swim tunnels.

Intraspecific differences in relation to migration distance and difficulty

The intraspecific differences in migration capacity were sometimes correlated with in-river migration distance and difficulty. For example, ES sockeye salmon, the furthest migrating stock of any of the Fraser River salmon, attained a significantly higher Ucrit at a 5°C cooler temperature and were more efficient swimmers at Ucrit because of a lower O2max compared with GC sockeye salmon. These attributes of ES sockeye salmon may be advantageous because they migrate almost three times the distance up the Fraser River compared with GC sockeye salmon(Table 1). Similarly, both ES and GC sockeye salmon migrate much longer distances and negotiate more severe hydraulic challenges compared with the coastal WVR sockeye salmon and correspondingly had a larger scope for activity at comparable water temperatures. Moreover, almost all of the GC and ES fish repeated their swimming performance without recovering O2 to within 5%of O2routine. The differences in swimming energetics found between CHE coho salmon and WVR sockeye also probably reflect species-specific adaptations. Yet, because these two salmon stocks face almost identical in-river migration distances and conditions, other factors must be involved in these adaptations. Thus, the suggestion that distance and/or difficulty of migration are powerful selective factors acting on salmonids (Bernatchez and Dodson, 1985 G. T. Crossin, S. G. Hinch, A. P. Farrell, D. A. Higgs, A. G. Lotto, J. D. Oakes and M. C. Healey, unpublished observations) is supported by the present study.

Intraspecific adaptation of maximum swimming ability has been previously established for juvenile salmonids either held or reared in a laboratory, but to our knowledge not for adult, wild salmon. For example, juvenile Pacific coho salmon from an interior river had an inheritable trait that resulted in a lower initial acceleration for a fast start, but a longer time-to-fatigue at a constant swimming speed compared with coho salmon from a coastal river(Taylor and McPhail, 1985). Similarly, Pacific steelhead trout O. mykiss from an interior river also had a greater time-to-fatigue for an incremental swimming speed test and allelic differences in the lactate dehydrogenases compared with a coastal stock (Tsuyuki and Williscroft,1977). Nevertheless, because Bams(1967) found that rearing conditions could alter swimming performance of sockeye salmon fry, phenotypic rather than genotypic expression could have contributed to the differences we observed.

Paradoxically, GC sockeye salmon were less efficient swimmers than either WVR sockeye salmon or CHE coho salmon, and why this is so is unclear. GC sockeye salmon were unusual in another regard, the plateau in O2 as the fish neared Ucrit. In fish, O2 typically increases exponentially with swimming speed (seeBeamish, 1978) to overcome drag, which is exponentially related to water velocity(Webb, 1975). Rarely is a plateau observed in O2 even though fish progressively increase the anaerobic contribution to swimming at 75%Ucrit (Brett and Groves, 1979 Burgetz et al.,1998). Consequently, the plateaus for both O2 and the cost of transport curve near Ucrit for GC sockeye salmon point to an unusually high contribution of anaerobically fueled locomotion. This possibility is further explored in the accompanying paper(Lee et al., 2003b), in which excess post-exercise oxygen consumption is examined as a measure of the anaerobic swimming activity.

In summary, we conclude that variation in O2routine among adult salmon stocks was primarily due to differences in water temperature. In contrast, distinct temperature optima for O2max were evident among salmon stocks, which when combined with differences in scope for activity and Ucrit, suggest stock-specific as well as species-specific differences in the temperature sensitivity of the physiological mechanisms that underpin oxygen delivery during swimming in adult Pacific salmon.

How Cells Grow

11.8 Oxygen Demand for Aerobic Microorganisms

Dissolved oxygen (DO) is an important substrate in aerobic fermentations and may be a limiting substrate , since oxygen gas is sparingly soluble in water. At high cell concentrations, the rate of oxygen consumption may exceed the rate of oxygen supply, leading to oxygen limitations. When oxygen is the rate-limiting factor, the specific growth rate varies with the DO concentration according to Monod equation, just like any other substrate-limited case.

Above a critical oxygen concentration, the growth rate becomes independent of the DO concentration. Fig. 11.6 depicts the variation of specific growth rate with dissolved a oxygen concentration in a rich medium (no other substrate limitation). Oxygen is a growth rate-limiting factor when the DO level is below the critical DO concentration. In this case, another medium component (eg, glucose, ammonium) becomes growth-extent limiting. For example, with Azotobacter vinelandii at a DO = 0.05 mg/L, the growth rate is about 50% of the maximum, even if a large amount of glucose is present. However, the maximum amount of cells formed is not determined by the DO, as oxygen is continually resupplied. If glucose were totally consumed, growth would cease, even if DO = 0.05 mg/L. Thus, the extent of growth (mass of cells formed) would depend on glucose, while the growth rate for most of the culture period would depend on the value of DO.

Fig. 11.6 . Growth rate dependence on dissolved oxygen (DO) for aerobic (A) and facultative organisms (B), Azotobacter vinelandii and E. coli, respectively. The lines are the Monod equation fit to the data (symbols).

Data from J. Chen, A.L. Tannahill and M.L. Shuler, Biotechnol. Bioeng., 27: 151, 1985.

As shown in Fig. 11.6 , the dependence of DO for aerobic and facultative organisms on cell growth follows the Monod growth equation. For aerobic organisms:

and for facultative organisms,

where μmax0 is the maximum specific growth rate in anaerobic conditions. Facultative organisms grow with or without oxygen. For anaerobic organisms, there is no growth if oxygen is present.

The critical oxygen concentration is about 5–10% of the saturated DO concentration for bacteria and yeast, and about 10–50% of the saturated DO concentration for mold cultures, depending on the pellet size of molds. Saturated DO concentration in water at 25 °C and 1 atm pressure is about 7 ppm. The presence of dissolved salts and organics can alter the saturation value, while increasingly high temperatures decrease the saturation value.

Oxygen is usually introduced to the fermentation broth by sparging air through the broth. Oxygen transfer from gas bubbles to cells is usually limited by oxygen transfer through the liquid film surrounding the gas bubbles. The rate of oxygen transfer from the gas to liquid phase is given by:

where kL is the oxygen transfer coefficient (m/h), a is the gas-liquid interfacial area (m 2 /m 3 ), C* is saturated DO concentration (g/L), CL is the actual DO concentration in the broth (g/L), and the N O 2 is the rate of oxygen transfer (g/L/h). Also, the term oxygen transfer rate (OTR) is used.

The rate of oxygen uptake is denoted as OUR (oxygen uptake rate) and:

where μ O 2 is the specific rate of oxygen consumption (g/g-cells/h), Y F X / O 2 is the yield factor on oxygen (g-cells/g-O2), and X is cell concentration (g-cells/L). When oxygen transfer is the rate-limiting step, the rate of oxygen consumption is equal to the rate of oxygen transfer. If the maintenance requirement of O2 is negligible compared to growth, then:

or in the batch reactor with negligible medium volume loss (due to air sparging):

Growth rate varies nearly linearly with the oxygen transfer rate under oxygen transfer limitations. Among the various methods used to overcome DO limitations are the use of oxygen-enriched air or pure oxygen and operation under high atmospheric pressure (2–3 atm). Oxygen transfer has a big impact on reactor design.

The maximum or saturation oxygen concentration is a function of temperature and oxygen pressure, as well as medium compositions. Electrolytes have a strong effect on oxygen solubility and transport. Quicker et al. (1981) gave a simple correlation between the saturation oxygen concentration (C*) and medium ionic and nonionic solute concentration:

where C0* is the oxygen saturation concentration in pure water ( Table 11.3 ), C* is the oxygen saturation concentration in the medium, Hj is the oxygen solubility interaction constants ( Table 11.4 ), Zj is the ionic charge of ionic species j, and Cj is the concentration of species j in the fermentation medium.

Table 11.3 . Solubility of Oxygen in Pure Water

Temperature (°C)Oxygen Solubility in Pure Water When Contacting with Air at 1 atm (kg/m 3 )Henry’s Law Constant (atm-O2/m 3 /kg − 1 )Density of Water (kg/m 3 )
01.46 × 10 − 2 14.4999.839
51.28 × 10 − 2 16.4999.964
101.14 × 10 − 2 18.4999.699
151.02 × 10 − 2 20.5999.099
200.929 × 10 − 2 22.6998.204
250.849 × 10 − 2 24.7997.045
300.782 × 10 − 2 26.9995.647
350.731 × 10 − 2 28.7994.032
400.692 × 10 − 2 30.4992.215
450.656 × 10 − 2 32.0990.213
500.627 × 10 − 2 33.5988.037
600.583 × 10 − 2 36.0983.200
700.550 × 10 − 2 38.2977.771
800.528 × 10 − 2 39.8971.799
900.515 × 10 − 2 40.8965.321
1000.510 × 10 − 2 41.2958.365

Calculated from International Critical Tables, 1928, vol. III, p. 257. McGraw-Hill: New York.

Should heart-to-body energy consumption ratio equal oxygen consumption ratio? - Biology

HIIT vs. Continuous Endurance Exercise: Metabolic Adaptations
Increasing mitochondrial density can be considered a skeletal muscle and metabolic adaptation. One focal point of interest for metabolic adaptations is with the metabolism of fat for fuel during exercise. Because of the nature of high intensity exercise, the effectiveness of this type of training for fat burning has been examined closely. Perry et al. (2008) showed that fat oxidation, or fat burning was significantly higher and carbohydrate oxidation (burning) significantly lower after 6 weeks of interval training. Similarly, but in as little as two weeks Talanian et al. (2007) showed a significant shift in fatty acid oxidation with HIIT. In their research review, Horowitz and Klein (2000) summarize that an increase in fatty acid oxidation is a noteworthy adaptation observed with continuous endurance exercise.

Another metabolic benefit of HIIT training is the increase in post-exercise energy expenditure referred to as Excess Post-exercise Oxygen Consumption (E.P.O.C.). Following an exercise session, oxygen consumption (and thus caloric expenditure) remains elevated as the working muscle cells restore physiological and metabolic factors in the cell to pre-exercise levels. This translates into higher and longer post-exercise caloric expenditure. In their review article, LaForgia, Withers, & Gore (2006) note that exercise intensity studies indicate higher E.P.O.C. values with HIIT training as compared to continuous aerobic training.

Final Verdict: And the Winner of the Battle of the Aerobic Titans is…
The major goals of most endurance exercise programs are to improve cardiovascular, metabolic, and skeletal muscle function in the body. For years continuous aerobic exercise has been the chosen method to achieve these goals. However, research shows that HIIT leads to similar and in some cases better improvements in shorter periods of time with some physiological markers. Incorporating HIIT (at the appropriate level of intensity and frequency) into a client's cardiovascular training allows exercise enthusiasts to reach their goals in a very time efficient manner. And, since both HIIT and continuous aerobic exercise programs improve all of these meaningful physiological and metabolic functions of the human body, incorporating a balance of both programs for clients in their training is clearly the 'win win' approach for successful cardiovascular exercise improvement and performance. GO HIIT and GO Endurance!

Side Bar 1. HIIT Program Development
When developing a HIIT program the duration, intensity, and frequency of the interval must be considered along with the length of the recovery interval. Duration of the work (high intensity effort) bout should be between 5 seconds to 8 minutes. Power athletes tend to perform shorter work intervals (5 sec - 30 sec) while endurance athletes will extend the high intensity work interval (30 sec - 8 min) (Kubukeli, Noakes & Dennis, 2002). Intensity during the high intensity work bout should range from 80% to greater than 100% of maximal oxygen consumption (VO2max), heart rate max, or maximal power output. The intensity of the recovery interval ranges from passive recoveries (doing very little movement) or active recoveries (which is more common) of about 50-70% of the above described intensity measures.

The relationship of the work and recovery interval is also a consideration. Many studies use a ratio of exercise to recovery, for example a ratio of 1:1 could be a 30-second interval followed by 30 seconds of recovery. A ratio of 1:2 would be a 30-second interval followed by a 1-minute rest. Typically, the ratios are designed in order to challenge a particular energy system of the body.
Read through the sample workouts that follow. These workouts have been used in previous research studies to induce both cardiovascular and skeletal muscle changes. Each component of a training session is included.

Sample 1: Track workout
Warm-up: Light 10-min run around track.
Interval: 800-meter runs at approximately 90% of maximal heart rate (based on estimation heart rate max = 220-age). Each 800-meter interval should be timed.
Rest Interval: Light jog or walk for same amount of time it took to run each 800 meter
Work/Rest ratio: 1 to 1 ratio. The time for the interval (800 meter) and rest interval should be the same.
Frequency: Try to complete 4 repetitions of this sequence.
Cool Down: 10-min easy jog.
Comments: The distance of the interval can be adjusted from 200 meter to 1000 meter. Also, the length of the rest interval can be adjusted.
Adapted from Musa et al. (2009).

Program 2: Sprint training workout
Warm-up: 10 min of light running.
Interval: 20-second sprints at maximal running speed.
Rest interval: 10 seconds of rest between each sprint. Light jogging or walking
Work/Rest ratio: 2 to 1 ratio. The work interval is 20-sec and rest interval is 10-sec.
Frequency: 3 groups or sets of 10-15 intervals. Take 4 min of rest between each set
Cool Down: 10 min easy jog
Comments: This is a sprint workout. The first few intervals should be slower allowing muscles to adapt to the workout. It is important to be safe and careful avoiding muscle damage during maximal sprinting exercise. The warm-up session is very important.
Adapted from Tabata et al. 1996.

Program 3: Treadmill workout
Warm-up: 10 min of light jogging.
Interval: Set treadmill incline at 5% grade and speed at 3 mph. During each high intensity interval increase speed to 5 mph - 6.5 mph, while keeping grade at 5%. The length of the interval should be 1 min.
Rest Interval: 2-minute rest interval with the walking speed set to 3 mph. Do not adjust incline.
Work/Rest Ratio: 1 to 2 ratio. The work interval is 1-minute and the rest interval is 2-minutes
Frequency: 6-8 repetitions of this sequence.
Cool Down: 5 - 10-minutes of easy jogging
Comments: This is a hill running interval session. Incline, running speed, interval length, and rest interval can be adjusted during the interval session.
Adapted from Seiler and Hetlelid, 2005.

Side Bar 2: Four Great Endurance Programs Ideas
The following 4 endurance exercise programs are adapted from the research investigations reviewed by LaForgia, Withers, & Gore, 2006. Perform an adequate warm-up (

10 min of light exercise) and cool-down (

5-10 min of low-intensity exercise) for each program. All of the workouts below can be performed on any aerobic mode.

1) Maximal lactate steady state exercise.
The maximal lactate steady state (MLSS) workout is the highest workload an exerciser can maintain over a specified period of time. MLSS exercise work bouts can last between 20 and 50 minutes. Tell the client to work at his/her maximal steady state of exercise for the desired time (between 20 and 50 minutes).

2) Alternating aerobic modes endurance exercise.
Alternate aerobic modes (i.e., treadmill and elliptical trainer) every 20 to 40 minutes of aerobic exercise keeping the exercise intensity &Mac17970% of heart rate max. Keep the time on each mode of exercise that same. Number of alternating modes is dependent on fitness level of client.

3) Step-wise endurance exercise.
With step-wise endurance exercise the client progresses from 10 minutes (at &Mac17950% heart rate max) to 10 minutes (at &Mac17960% heart rate max) to 10 minutes (at &Mac17970% heart rate max) on any aerobic mode. For a slight modification, the personal trainer may have the client step-wise up in intensity and also step-wise down on this workout. Thus, after completing the 10 min at a 70% heart rate max the client would switch to 10 minutes at 60% heart rate max and then 10 minutes at 50% heart rate max.

4) Mixed-paced endurance exercise.
On the selected mode of exercise randomly vary the endurance duration (i.e., 5 min, 10 min, 15 min blocks of time) and the intensity of exercise. For instance, a 45 minute endurance treadmill workout could begin with 10 min at 50% heart rate max, then sequence into 5 min at 70% heart rate max, then 15 min at 60% heart rate max, then 10 min at 75% heart rate max, and finish with 5 min at 50% heart rate max.

Fact Box 1: Seven Remarkable Endurance Feats of Interest
1. The official International Association of Athletics Federations world Marathon record for men is 2:03:59, set by Haile Gebrselassie of Ethiopia on September 28, 2008 at the Berlin Marathon.

2. The women's record holder in the marathon is Paula Radcliffe of the United Kingdom in a time of 2:15:25.

3. The predicted human capability of the marathon based on physiological characteristics as describe by Joyner (1991) is 1:57:58. This equals a 4:30 per mile pace.

4. The longest certified road race in the world is the 3100 mile Self-Transcendence Race in New York City that takes place around a half-mile city block in Queens, NY. Only 30 runners have completed the race, which requires each contestant to complete 2 marathons per day for 50 days.

5. The longest bicycle race is the Tour d'Afrique, which is 12,000 km (7500 miles) and 120 days traveling from Cairo, Egypt to Cape Town, South Africa.

6. One of the longest swims ever was recorded by Martin Strel in 2009. The Slovenian man swam the length of the Amazon River (3,272 miles) in 66 days.

7. Another outstanding swimming feat was a performance by Benoit Lecomte, who swam across the Atlantic Ocean from Cape Cod to France, which is 3,736 miles. He averaged six to eight hours per day of swimming.

Side Bar 3: Four Important Questions and Answers Regarding HIIT
1. How many times per week can HIIT be completed?
A: Research says that three times per week may produce the best results while limiting injury (Daussin et al., 2008 Helgerud, et al., 2007 Musa, et al., 2009 Perry, et al., 2008). Interval training is very demanding and it is important to be fully recovered between sessions.

2. Barefoot running has grown in popularity in the past several years. Is it safe to perform HIIT barefoot?
A: According to Pauls and Kravitz (2010) it is important to progress slowly into barefoot running regardless of intensity. The best method may be to perform other daily living activities such as walking, cleaning, or gardening before beginning to run. Once you are consistently barefoot running there should be another progression with HIIT. Begin with one or two barefoot intervals and increasing to three or four over a several weeks is the best recommendation.

3. If a client has been inactive for several months is it safe to start an exercise program with HIIT?
A: There should be a careful progression of activity when re-starting any exercise program. Beginning with HIIT may increase the chance for injury and muscle soreness. A better approach would be to start with continuous aerobic exercise at a low intensity level. Once the client is able to run for thirty consecutive minutes at a moderate intensity he/she can then progress slowly into interval training.

4. Is it O.K. to before a high intensity interval training session?
A: When exercise intensity is greater than 80% the rate at which the contents of stomach empty slow down (de Oliveira & Burini, 2009). This is due to changes in blood flow along with hormonal and neurotransmitter activation. To reduce the chance of gut problems during exercise eat foods that are low in fiber, lactose, and nutritive sweeteners several hours before a training session, and be sure to drink plenty of fluids (de Oliveira & Burini, 2009).
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Burgomaster, K.A., Howarth, K.R., Phillips, S.M., Rakobowchuk, M., Macdonald, M.J., McGee, S.L., and Gibala, M.J. (2008). Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans. Journal of Physiology, 586(1), 151-160.

Daussin, F.N., Zoll, J., Dufour, S.P., Ponsot, E., Lonsdorfer-Wolf, E. et al. (2008). Effect of interval versus continuous training on cardiorespiratory and mitochondrial functions relationship to aerobic performance improvements in sedentary subjects, American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 295, (R264-R272.

de Oliveira, E.P. and Burini, R.C. (2009). The impact of physical exercise on the gastrointestinal tract. Current Opinion in Clinical Nutrition and Metabolic Care, 12(5), 533-538.

Gibala, M. (2009). Molecular responses to high-intensity interval exercise. Applied Physiology, Nutrition, and Metabolism, 34(3), 428-432.

Helgerud, J., Høydal, K., Wang, E., Karlsen, T., Berg, P., et al. (2007). Aerobic high-intensity intervals improve VO2max more than moderate training. Medicine and Science in Sports and Exercise, 39(4), 665-671.

Horowitz J.F. and Klein S. (2000). Lipid metabolism during endurance exercise. American Journal of Clinical Nutrition. 72(2 Suppl), 558S-563S.

Joyner M.J. (1991). Modeling: optimal marathon performance on the basis of physiological factors. Journal of Applied Physiology. 70(2), 683-687.

Joyner, M.J. and Coyle, E.F. (2008). Endurance exercise performance: the physiology of champions. Journal of Applied Physiology, 586 (1), 35-44.

Kubukeli, Z.N., Noakes, T.D., and Dennis, S.C. (2002). Training techniques to improve endurance exercise performances. Sports Medicine, 32(8), 489-509.

LaForgia, J., Withers, R.T., and Gore, C.J. (2006). Effects of exercise intensity and duration on the excess post-exercise oxygen consumption. Journal of Sports Science, 24(12), 1247-1264.

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Musa, D.I., Adeniran, S.A., Dikko, A.U., and Sayers, S.P. (2009). The effect of a high-intensity interval training program on high-density lipoprotein cholesterol in young men. Journal of Strength and Conditioning Research, 23(2), 587-592.

Pauls, C. and Kravitz, L. (2010). Barefoot running: An exciting new training dimension to consider for certain clients. IDEA Fitness Journal, 7(4), 18-20.

Pavlik, G., Major, Z., Varga-Pintér, B., Jeserich, M., & Kneffel, Z. (2010). The athlete's heart Part I (Review). Acta Physiologica Hungarica, 97(4), 337-353.

Perry, C.G.R, Heigenhauser, G.J.F, Bonen, A., and Spriet, L.L. (2008). High-intensity aerobic interval training increases fat and carbohydrate metabolic capacities in human skeletal muscle. Applied Physiology, Nutrition, and Metabolism, 33(6), 1112-1123.

Seiler, S., and Hetlelid, K.J. (2005). The impact of rest duration on work intensity and RPE during interval training. Medicine and Science in Sports and Exercise, 37(9), 1601-1607.

Slørdahl, S.A., Madslien, V.O., Støylen, A., Kjos, A., Helgerud, J., and Wisløff, U. (2004). Atrioventricular plane displacement in untrained and trained females. Medicine and Science in Sports and Exercise, 36(11), 1871-1875.

Tabata, I., Nishimura, K., Kouzaki, M., Hirai, Y., Ogita, F., Miyachi, M., and Yamamoto, K. (1996). Effects of moderate-intensity endurance and high-intensity intermittent training on anaerobic capacity and VO2max. Medicine and Science in Sports and Exercise, 28(10), 1327-1330.

Talanian, J.L., Galloway, S.D., Heigenhauser, G.J, Bonen, A., and Spriet, L.L. (2007). Two weeks of high-intensity aerobic interval training increases the capacity for fat oxidation during exercise in women. Journal of Applied Physiology, 102(4), 1439-1447.

Wisløff, U., Ellingsen, Ø., and Kemi, O. J. (2009). High-intensity interval training to maximize cardiac benefits of exercise training? Exercise Sport Science Review, 37(3), 139-146.

Micah Zuhl, Ph.D. is an assistant professor in the Department of Health Sciences at Central Michigan University. His research interests include the GI tract and exercise, sports performance and disease prevention through exercise. Micah is a recreational cyclist, runner, and hiker.

Len Kravitz, PhD, is the program coordinator of exercise science and a researcher at the University of New Mexico, where he won the Outstanding Teacher of the Year award. He has received the prestigious Can-Fit-Pro Lifetime Achievement Award and was chosen as the American Council on Exercise 2006 Fitness Educator of the Year.

Supporting information

S1 Table. AHRQ Cross-Sectional/Prevalence study quality assessment checklist.

Y yes, N no, N.A. not applicable, ? not specified/unknown. *1. Was the source of information for the reported outcome measurements mentioned? 2.Were inclusion criteria reported? 3. Were exclusion criteria reported? 4. Was the timeframe of recruitment reported? 5. Were subjects consecutively recruited or population-based? 6. Were evaluators of subjective components masked to other aspects of the subjects? 7. Have any assessments been undertaken for quality assurance purposes (test/retest of primary outcome measurements)? 8. Was the used equipment validated or were there references to validation in previous publications? 9. Were all participants included in the analysis? 10.Was confounding assessed and/or controlled for? 11. Were missing data reported? 12. Were patient response rate and completeness of data collection reported? 13. Were follow-up, incomplete data, or loss to follow-up reported? 14. Was tested walking speed reported?

S2 Table. Frequencies of the reported outcome variables.

HR heart rate PCI physiological cost index %HRR % heart rate reserve RER respiratory exchange ratio RR respiratory rate BP blood pressure METs metabolic equivalent of task RQ respiratory quotient EEI energy expenditure index.